1 Libraries and environment
1.1 Load environment
Libraries used to create and generate this report:
- R :
R version 4.2.1 (2022-06-23) - rmarkdown :
2.22 - knitr :
1.43 - rmdformats :
1.0.4 - bookdown :
0.34 - kableExtra :
1.3.4
1.2 Load libraries
Libraries used for data analysis:
library("DESeq2")
library("limma")
library("FactoMineR")
library("factoextra")
library("ggplot2")
library("pheatmap")
library("rRoma")
library("clusterProfiler")
library("org.Hs.eg.db")
library("ggrepel")
library("dendextend")- DESeq2:
1.38.3 - limma:
3.54.2 - FactoMineR:
2.8 - ggplot2:
3.4.2 - pheatmap:
1.0.12 - clusterProfiler:
4.6.2 - org.Hs.eg.db:
3.16.0 - rRoma:
0.0.4.2000
2 Description of the RNA-seq dataset
In this tutorial, we will try a typical workflow for RNA-sequencing data analysis. We will use the TB dataset, which consists of gene expression profiles on 41 tumor biopsies from cancer patients.
- RNA-seq is the high-troughput sequencing of cDNA using NGS technologies
- RNA-seq works by sequencing evry RNA molecule and profiling the expression of genes by counting the number of times their transcripts have been sequenced.
- The summarized RNA-seq output is a count matrix.
You can read more about how the data was processed here. In summary, it follows a general outline as in the figure below: first, we have a set of reads. Using an aligner and a reference genome, we determine the origin of these reads in terms of the chromosome and position within the reference genome. Then, we compare this information with a reference annotation that provides the chromosome and position details of exons in known genes.
3 Input data
In this matrix, each row represents a gene, each column a patient sample and the values provide the raw gene expression count numbers.
rna <- read.csv("data_tb/data_rna_count.csv", header = TRUE, row.names = 1)
dim(rna)## [1] 19690 41We have some information about the sample in the sample_annotation file. We downlaod prior knowledge of experimental design for patient samples.
design <- read.csv("data_tb/design.csv", header = TRUE, row.names = 1)
design <- design[colnames(rna),]
hospital <- as.factor(design$Hospital)
group <- as.factor(design$Group)
design$Hospital <- as.factor(design$Hospital)
design$Group <- as.factor(design$Group)Tumor biopsies are collected from different hospitals and assigned to a unique sample ID. Clinical classification of these tumors based primarily on morphology (histopathology) and available clinicopathological parameters identified four different disease subgroups that are associated to different outcomes and are used to direct treatment strategies.
With recent developments in omics profiling, there have been significant advancements in the field of tumor classification, leading to a deeper understanding of the molecular characteristics and heterogeneity of tumors. This enhanced classification allows for more precise and personalized treatment strategies, as it helps to identify predictive biomarkers and potential therapeutic targets specific to each tumor subgroup.
4 Quality control of raw counts
Our count matrix contains many rows with only zeros, and additionally many rows with only a few count total. In order to reduce the size of the matrix, and to increase the speed of our analyses, we can remove the rows that have no or nearly no information about the amount of gene expression.
g.zero <- rowSums(rna == 0) # Number of zero counts per gene
s.zero <- colSums(rna == 0) # Number of features (genes) with zero counts per sample
barplot(colSums(rna), main = "Total counts per sample", ylab = "Total counts", las=1, las=2, cex.axis= 0.8, cex.names=0.8 )
grid()
barplot(s.zero/nrow(rna), main = "Proportion of null features per sample", ylab = "Proportion of null counts", las=2, cex.names=0.8 )
grid()
dim(rna)## [1] 19690 41keep <- rowSums(rna) >= 10 # Keep only rows that have at least 10 reads total.
rna <- rna[keep,]
dim(rna)## [1] 18241 415 Normalization
Box plot for the gene expression data before normalization. The black bar indicates the median value. These distributions need to be similar for the different samples to be comparable. If this is not the case, the data should be normalised.
boxplot(log(rna + 1))
We create a DESeq object from the data matrix and apply normalization by DESeq method
dds <- DESeqDataSetFromMatrix(countData = rna, colData = design, design = ~ Hospital+Group)
### DESeq normalization
dds <- estimateSizeFactors(dds)
dds <- estimateDispersions(dds)
rna.norm <- counts(dds, normalized=TRUE)
boxplot(log(rna.norm+1))
log.rna.norm <- log(rna.norm+1)6 Principal component analysis
We can use principal component analysis (PCA) for reducing our large scale data matrix into few major components. Exploratory analysis by PCA is crucial for quality control and gaining initial insights into our dataset. It can help us detect quality problems, sample swaps and contamination, as well as give us a sense of the most salient patterns present in the data, their structure and variability.
We use FactoMineR, an R package dedicated to multidimensional exploratory analysis of data.
pca.rna <- PCA(t(log.rna.norm), graph = FALSE)6.1 Explore PCA results
The proportion of variances retained by the principal components can be visualized by with a scree plot, which is a graph of the eigenvalues/variances associated with components.
## display eigen values
fviz_eig(pca.rna)
Other functions are available to visualize samples in the PCA space:
## display samples
fviz_pca_ind(pca.rna, habillage = design$Group)
fviz_pca_ind(pca.rna, habillage = design$Group, axes = c(3,4))
Question: how many tumor subgroups do you observe?
We can further explore which of the original variables are contributing the most to the various PCs by inspecting their barplot.
## most contributing genes
fviz_contrib(pca.rna, choice = "var", top = 30, axes = 1)
fviz_contrib(pca.rna, choice = "var", top = 30, axes = 2)
7 Clustering
Unsupervised clustering methods are widely used for data exploration and generating hypotheses. The aim is to partition data according to natural classes present in it, assigning samples that are “more similar” to the same “cluster”.
To perform clustering, the first step is to compute the distance between each sample:
d <- dist(t(log.rna.norm))We can perform hierarchical clustering based on the distances defined above using the hclust function. This function returns an hclust object that describes the groupings that were created using the algorithm described above. The plot method represents these relationships with a tree or dendrogram:
hc <- hclust(d, method = "ward.D")
plot(hc, hang = -1)
dend <- as.dendrogram(hc)
colored_bars(as.numeric(design$Group), dend = dend)
Based on the dendogram, we can cut the tree into k clusters that represents our classes.
8 Differential expression for RNA-seq
The standard differential expression analysis steps are performed by a single function, DESeq. Results tables are generated using the function results, which extracts a results table with log2 fold changes, p values and adjusted p values. We can select significantly different expressed genes according to a given p-value threshold
dds <- DESeq(dds)
res <- results(dds, contrast = c("Group", "G4", "G3"))
sum(res$padj<=0.05, na.rm = TRUE)## [1] 40658.1 Heatmap visualization
We can visualize our results with a heatmap. Here we display the top 50 most differentially expressed genes.
res <- res[order(res$padj),]
top50 <- res[1:50, ]
top50.mat <- log.rna.norm[rownames(top50),]
pheatmap(top50.mat, scale="row", annotation_col = design, clustering_distance_rows = "correlation", clustering_method = "ward.D")
9 Downstream analysis and gene-set testing
The volcano plot is a classical representation of the results of a differential expression analysis. In such a plot we display the -log10 of the pvalue as a function of the log2 fold change. Here we also display the names of the 10 genes with the lowest pvalues as an example.
res_df <- as.data.frame(res)
res_df$symbol <- rownames(res_df)
ggplot(as.data.frame(res), aes(x = log2FoldChange, y = -log10(pvalue))) +
geom_point() +
geom_label_repel(aes(label = symbol), data = res_df[1:10,]) +
theme_light()
To interpret the list of differentially expressed genes, we can look for common biological functions among them. Several databases exist to link genes and biological functions, one the most used is Gene Ontology (GO). The enrichGO function from clusterProfiler will search for gene sets that are enriched in GO categories.
## select list of differentially expressed genes with sufficient logFC
genes_signif <- rownames(res)[which(res$padj <= 0.05 & abs(res$log2FoldChange)>=1)]
res_GO <- enrichGO(gene = genes_signif, OrgDb = "org.Hs.eg.db", keyType = "SYMBOL", universe = rownames(res), ont = "BP")
as.data.frame(res_GO)## ID
## GO:0002181 GO:0002181
## GO:0042255 GO:0042255
## GO:0007156 GO:0007156
## GO:0042273 GO:0042273
## GO:0098742 GO:0098742
## GO:0042274 GO:0042274
## GO:0000027 GO:0000027
## GO:0042254 GO:0042254
## GO:0034765 GO:0034765
## GO:0050804 GO:0050804
## GO:0099177 GO:0099177
## GO:0098657 GO:0098657
## GO:0061564 GO:0061564
## GO:0050808 GO:0050808
## GO:0006364 GO:0006364
## GO:0071805 GO:0071805
## GO:0097485 GO:0097485
## GO:0001764 GO:0001764
## GO:0006813 GO:0006813
## GO:0050878 GO:0050878
## GO:0016072 GO:0016072
## GO:0007411 GO:0007411
## GO:0007409 GO:0007409
## GO:0006836 GO:0006836
## GO:0043270 GO:0043270
## GO:0030900 GO:0030900
## GO:2000027 GO:2000027
## GO:0000028 GO:0000028
## GO:0042391 GO:0042391
## GO:1904062 GO:1904062
## GO:0048880 GO:0048880
## GO:0098659 GO:0098659
## GO:0099587 GO:0099587
## GO:0021953 GO:0021953
## GO:0007589 GO:0007589
## GO:0001505 GO:0001505
## GO:0150063 GO:0150063
## GO:1904667 GO:1904667
## GO:0098739 GO:0098739
## GO:0006936 GO:0006936
## GO:0001654 GO:0001654
## GO:0032412 GO:0032412
## GO:0006865 GO:0006865
## GO:0022613 GO:0022613
## GO:0022898 GO:0022898
## GO:0030198 GO:0030198
## GO:0098656 GO:0098656
## GO:0043062 GO:0043062
## GO:0015701 GO:0015701
## GO:0045229 GO:0045229
## GO:0010959 GO:0010959
## GO:0021537 GO:0021537
## GO:0030879 GO:0030879
## GO:0048568 GO:0048568
## GO:0023061 GO:0023061
## GO:1990573 GO:1990573
## GO:0070252 GO:0070252
## GO:0002027 GO:0002027
## GO:0048708 GO:0048708
## GO:0010975 GO:0010975
## GO:0043931 GO:0043931
## GO:0000462 GO:0000462
## GO:0007595 GO:0007595
## GO:0140694 GO:0140694
## GO:1901796 GO:1901796
## GO:0007193 GO:0007193
## GO:0015800 GO:0015800
## GO:0007157 GO:0007157
## GO:0051444 GO:0051444
## GO:0021534 GO:0021534
## GO:0021924 GO:0021924
## GO:0021930 GO:0021930
## GO:0070977 GO:0070977
## GO:0048799 GO:0048799
## GO:0042363 GO:0042363
## GO:1904816 GO:1904816
## GO:0042476 GO:0042476
## GO:0014068 GO:0014068
## GO:1904064 GO:1904064
## GO:0032409 GO:0032409
## GO:0086003 GO:0086003
## GO:0003012 GO:0003012
## GO:0086011 GO:0086011
## GO:0001504 GO:0001504
## GO:0030490 GO:0030490
## GO:0043266 GO:0043266
## GO:0015844 GO:0015844
## Description
## GO:0002181 cytoplasmic translation
## GO:0042255 ribosome assembly
## GO:0007156 homophilic cell adhesion via plasma membrane adhesion molecules
## GO:0042273 ribosomal large subunit biogenesis
## GO:0098742 cell-cell adhesion via plasma-membrane adhesion molecules
## GO:0042274 ribosomal small subunit biogenesis
## GO:0000027 ribosomal large subunit assembly
## GO:0042254 ribosome biogenesis
## GO:0034765 regulation of ion transmembrane transport
## GO:0050804 modulation of chemical synaptic transmission
## GO:0099177 regulation of trans-synaptic signaling
## GO:0098657 import into cell
## GO:0061564 axon development
## GO:0050808 synapse organization
## GO:0006364 rRNA processing
## GO:0071805 potassium ion transmembrane transport
## GO:0097485 neuron projection guidance
## GO:0001764 neuron migration
## GO:0006813 potassium ion transport
## GO:0050878 regulation of body fluid levels
## GO:0016072 rRNA metabolic process
## GO:0007411 axon guidance
## GO:0007409 axonogenesis
## GO:0006836 neurotransmitter transport
## GO:0043270 positive regulation of ion transport
## GO:0030900 forebrain development
## GO:2000027 regulation of animal organ morphogenesis
## GO:0000028 ribosomal small subunit assembly
## GO:0042391 regulation of membrane potential
## GO:1904062 regulation of cation transmembrane transport
## GO:0048880 sensory system development
## GO:0098659 inorganic cation import across plasma membrane
## GO:0099587 inorganic ion import across plasma membrane
## GO:0021953 central nervous system neuron differentiation
## GO:0007589 body fluid secretion
## GO:0001505 regulation of neurotransmitter levels
## GO:0150063 visual system development
## GO:1904667 negative regulation of ubiquitin protein ligase activity
## GO:0098739 import across plasma membrane
## GO:0006936 muscle contraction
## GO:0001654 eye development
## GO:0032412 regulation of ion transmembrane transporter activity
## GO:0006865 amino acid transport
## GO:0022613 ribonucleoprotein complex biogenesis
## GO:0022898 regulation of transmembrane transporter activity
## GO:0030198 extracellular matrix organization
## GO:0098656 anion transmembrane transport
## GO:0043062 extracellular structure organization
## GO:0015701 bicarbonate transport
## GO:0045229 external encapsulating structure organization
## GO:0010959 regulation of metal ion transport
## GO:0021537 telencephalon development
## GO:0030879 mammary gland development
## GO:0048568 embryonic organ development
## GO:0023061 signal release
## GO:1990573 potassium ion import across plasma membrane
## GO:0070252 actin-mediated cell contraction
## GO:0002027 regulation of heart rate
## GO:0048708 astrocyte differentiation
## GO:0010975 regulation of neuron projection development
## GO:0043931 ossification involved in bone maturation
## GO:0000462 maturation of SSU-rRNA from tricistronic rRNA transcript (SSU-rRNA, 5.8S rRNA, LSU-rRNA)
## GO:0007595 lactation
## GO:0140694 non-membrane-bounded organelle assembly
## GO:1901796 regulation of signal transduction by p53 class mediator
## GO:0007193 adenylate cyclase-inhibiting G protein-coupled receptor signaling pathway
## GO:0015800 acidic amino acid transport
## GO:0007157 heterophilic cell-cell adhesion via plasma membrane cell adhesion molecules
## GO:0051444 negative regulation of ubiquitin-protein transferase activity
## GO:0021534 cell proliferation in hindbrain
## GO:0021924 cell proliferation in external granule layer
## GO:0021930 cerebellar granule cell precursor proliferation
## GO:0070977 bone maturation
## GO:0048799 animal organ maturation
## GO:0042363 fat-soluble vitamin catabolic process
## GO:1904816 positive regulation of protein localization to chromosome, telomeric region
## GO:0042476 odontogenesis
## GO:0014068 positive regulation of phosphatidylinositol 3-kinase signaling
## GO:1904064 positive regulation of cation transmembrane transport
## GO:0032409 regulation of transporter activity
## GO:0086003 cardiac muscle cell contraction
## GO:0003012 muscle system process
## GO:0086011 membrane repolarization during action potential
## GO:0001504 neurotransmitter uptake
## GO:0030490 maturation of SSU-rRNA
## GO:0043266 regulation of potassium ion transport
## GO:0015844 monoamine transport
## GeneRatio BgRatio pvalue p.adjust qvalue
## GO:0002181 98/2229 158/15817 2.538122e-44 1.505360e-40 1.405318e-40
## GO:0042255 28/2229 58/15817 4.922821e-10 1.459863e-06 1.362844e-06
## GO:0007156 53/2229 165/15817 2.471192e-09 4.885546e-06 4.560867e-06
## GO:0042273 31/2229 75/15817 6.820617e-09 1.011327e-05 9.441170e-06
## GO:0098742 72/2229 263/15817 1.022653e-08 1.213072e-05 1.132454e-05
## GO:0042274 29/2229 75/15817 1.200310e-07 1.121182e-04 1.046671e-04
## GO:0000027 15/2229 25/15817 1.323263e-07 1.121182e-04 1.046671e-04
## GO:0042254 75/2229 298/15817 2.271932e-07 1.684353e-04 1.572416e-04
## GO:0034765 109/2229 486/15817 3.221428e-07 2.122921e-04 1.981838e-04
## GO:0050804 96/2229 422/15817 8.319703e-07 4.934416e-04 4.606488e-04
## GO:0099177 96/2229 423/15817 9.303482e-07 5.016268e-04 4.682901e-04
## GO:0098657 61/2229 238/15817 1.561004e-06 7.715263e-04 7.202528e-04
## GO:0061564 104/2229 474/15817 1.763874e-06 8.047337e-04 7.512533e-04
## GO:0050808 93/2229 419/15817 3.720523e-06 1.576173e-03 1.471425e-03
## GO:0006364 56/2229 219/15817 4.445312e-06 1.757676e-03 1.640866e-03
## GO:0071805 53/2229 204/15817 4.818374e-06 1.786111e-03 1.667411e-03
## GO:0097485 58/2229 232/15817 6.428523e-06 2.242798e-03 2.093748e-03
## GO:0001764 47/2229 176/15817 7.393306e-06 2.402239e-03 2.242593e-03
## GO:0006813 57/2229 228/15817 7.695590e-06 2.402239e-03 2.242593e-03
## GO:0050878 81/2229 359/15817 8.168310e-06 2.422312e-03 2.261332e-03
## GO:0016072 62/2229 256/15817 9.341572e-06 2.638327e-03 2.462991e-03
## GO:0007411 57/2229 231/15817 1.170663e-05 3.156001e-03 2.946262e-03
## GO:0007409 92/2229 428/15817 1.628615e-05 4.199703e-03 3.920602e-03
## GO:0006836 52/2229 208/15817 1.892956e-05 4.531275e-03 4.230139e-03
## GO:0043270 65/2229 278/15817 1.927060e-05 4.531275e-03 4.230139e-03
## GO:0030900 82/2229 373/15817 1.986396e-05 4.531275e-03 4.230139e-03
## GO:2000027 36/2229 127/15817 2.082378e-05 4.574290e-03 4.270296e-03
## GO:0000028 10/2229 17/15817 2.278974e-05 4.827356e-03 4.506543e-03
## GO:0042391 88/2229 409/15817 2.378506e-05 4.864454e-03 4.541176e-03
## GO:1904062 80/2229 364/15817 2.520895e-05 4.953832e-03 4.624614e-03
## GO:0048880 84/2229 387/15817 2.589257e-05 4.953832e-03 4.624614e-03
## GO:0098659 33/2229 114/15817 2.838788e-05 5.102077e-03 4.763007e-03
## GO:0099587 33/2229 114/15817 2.838788e-05 5.102077e-03 4.763007e-03
## GO:0021953 44/2229 171/15817 3.836163e-05 6.691848e-03 6.247126e-03
## GO:0007589 25/2229 78/15817 4.077401e-05 6.909447e-03 6.450264e-03
## GO:0001505 52/2229 214/15817 4.321166e-05 6.983417e-03 6.519318e-03
## GO:0150063 82/2229 381/15817 4.403222e-05 6.983417e-03 6.519318e-03
## GO:1904667 8/2229 12/15817 4.474285e-05 6.983417e-03 6.519318e-03
## GO:0098739 47/2229 188/15817 4.663046e-05 7.091417e-03 6.620141e-03
## GO:0006936 73/2229 331/15817 4.974009e-05 7.375211e-03 6.885075e-03
## GO:0001654 81/2229 377/15817 5.202941e-05 7.526498e-03 7.026308e-03
## GO:0032412 60/2229 261/15817 6.652474e-05 9.394243e-03 8.769928e-03
## GO:0006865 37/2229 140/15817 8.419533e-05 1.161308e-02 1.084131e-02
## GO:0022613 91/2229 442/15817 9.751629e-05 1.296130e-02 1.209993e-02
## GO:0022898 61/2229 270/15817 9.834071e-05 1.296130e-02 1.209993e-02
## GO:0030198 66/2229 299/15817 1.087884e-04 1.402661e-02 1.309444e-02
## GO:0098656 58/2229 255/15817 1.188275e-04 1.490806e-02 1.391731e-02
## GO:0043062 66/2229 300/15817 1.206520e-04 1.490806e-02 1.391731e-02
## GO:0015701 12/2229 27/15817 1.317322e-04 1.594497e-02 1.488531e-02
## GO:0045229 66/2229 302/15817 1.479920e-04 1.755481e-02 1.638816e-02
## GO:0010959 81/2229 389/15817 1.568826e-04 1.824452e-02 1.703204e-02
## GO:0021537 56/2229 247/15817 1.686828e-04 1.923958e-02 1.796097e-02
## GO:0030879 34/2229 130/15817 1.989948e-04 2.226864e-02 2.078873e-02
## GO:0048568 88/2229 434/15817 2.175041e-04 2.374094e-02 2.216318e-02
## GO:0023061 90/2229 446/15817 2.201570e-04 2.374094e-02 2.216318e-02
## GO:1990573 17/2229 49/15817 2.333411e-04 2.471332e-02 2.307094e-02
## GO:0070252 27/2229 96/15817 2.472061e-04 2.551648e-02 2.382072e-02
## GO:0002027 28/2229 101/15817 2.495288e-04 2.551648e-02 2.382072e-02
## GO:0048708 23/2229 77/15817 2.670971e-04 2.685004e-02 2.506566e-02
## GO:0010975 87/2229 431/15817 2.751597e-04 2.719953e-02 2.539193e-02
## GO:0043931 11/2229 25/15817 2.803486e-04 2.722754e-02 2.541808e-02
## GO:0000462 14/2229 37/15817 2.934093e-04 2.722754e-02 2.541808e-02
## GO:0007595 14/2229 37/15817 2.934093e-04 2.722754e-02 2.541808e-02
## GO:0140694 77/2229 373/15817 2.977323e-04 2.722754e-02 2.541808e-02
## GO:1901796 28/2229 102/15817 2.983966e-04 2.722754e-02 2.541808e-02
## GO:0007193 22/2229 73/15817 3.132815e-04 2.815262e-02 2.628167e-02
## GO:0015800 20/2229 64/15817 3.407865e-04 3.016723e-02 2.816240e-02
## GO:0007157 15/2229 42/15817 3.754005e-04 3.223666e-02 3.009430e-02
## GO:0051444 10/2229 22/15817 3.860899e-04 3.223666e-02 3.009430e-02
## GO:0021534 8/2229 15/15817 3.913404e-04 3.223666e-02 3.009430e-02
## GO:0021924 8/2229 15/15817 3.913404e-04 3.223666e-02 3.009430e-02
## GO:0021930 8/2229 15/15817 3.913404e-04 3.223666e-02 3.009430e-02
## GO:0070977 11/2229 26/15817 4.244068e-04 3.448160e-02 3.219005e-02
## GO:0048799 12/2229 30/15817 4.353540e-04 3.489303e-02 3.257414e-02
## GO:0042363 7/2229 12/15817 4.513560e-04 3.511623e-02 3.278250e-02
## GO:1904816 7/2229 12/15817 4.513560e-04 3.511623e-02 3.278250e-02
## GO:0042476 32/2229 125/15817 4.559012e-04 3.511623e-02 3.278250e-02
## GO:0014068 22/2229 75/15817 4.761589e-04 3.620639e-02 3.380022e-02
## GO:1904064 39/2229 163/15817 5.141942e-04 3.860361e-02 3.603812e-02
## GO:0032409 61/2229 287/15817 5.581850e-04 4.112883e-02 3.839552e-02
## GO:0086003 21/2229 71/15817 5.616987e-04 4.112883e-02 3.839552e-02
## GO:0003012 83/2229 417/15817 5.925271e-04 4.285705e-02 4.000889e-02
## GO:0086011 11/2229 27/15817 6.256207e-04 4.470550e-02 4.173449e-02
## GO:0001504 15/2229 44/15817 6.637608e-04 4.641541e-02 4.333077e-02
## GO:0030490 17/2229 53/15817 6.652015e-04 4.641541e-02 4.333077e-02
## GO:0043266 27/2229 102/15817 7.062726e-04 4.870817e-02 4.547116e-02
## GO:0015844 23/2229 82/15817 7.197003e-04 4.906370e-02 4.580307e-02
## geneID
## GO:0002181 RPS7/RPS18/RPL12/RPL5/RPS2/RPL26/RPS6/RPS8/RPL22L1/RPL35/RPL7/RPL6/RPL17/EIF3E/RPS15A/EIF4B/RPL10A/RPL13/RPS17/RPL23/RPL11/RPL4/RPL13A/RPL7A/RPS3/RPLP0/PABPC1/RPL18/RPL9/RPS19/RBM24/RPL18A/RPL36/RACK1/RPL29/RPL14/RPL8/RPL41/RPS27/RPL35A/RPS25/RPS14/RPL31/RPS20/RPS24/EIF2S3/RPL38/RPL37/EIF3H/RPL27A/RPS3A/EIF3A/RPL3/RPS12/RPS9/RPL19/RPL32/RPS5/RPL34/RPS27A/RPL39/RPL30/RPL22/RPL24/RPL21/RPS23/RPL36A/RPS21/DPH5/RPL27/RPL15/RPS13/EIF3D/RPS4X/RPS16/RPS28/RPL37A/RPLP2/RPLP1/RPL28/RPS11/RPSA/IGF2BP1/DPH2/RPS15/CPEB4/RPS29/LIN28A/RPL23A/UBA52/EIF2D/EIF3M/RPS26/FAU/RPL10/CPEB2/YBX3/EIF3C
## GO:0042255 RPL5/NPM1/RPL6/RPL11/RPLP0/RPS19/RPS27/RPS14/NOP53/RRS1/RPL38/EIF2A/RPS5/NIP7/RPL24/RPF2/MRTO4/RPS28/RPSA/RPS15/RPL23A/BOP1/PPAN/MDN1/PRKDC/C1QBP/RPL10/PWP2
## GO:0007156 ROBO1/CNTN6/ROBO3/PCDHGC3/BSG/IGSF21/PCDHGC4/PALLD/PCDH1/PVR/PCDHB7/PCDH7/KIRREL3/AMIGO2/CDH4/PLXNB3/AMIGO1/PCDHB8/CADM4/NECTIN4/DCHS1/PCDHA2/FAT1/PCDHB6/PCDHA1/PCDH9/PCDH11Y/PCDHGA2/PCDHGB6/CDHR3/MYOT/PCDHGC5/CDHR1/FAT4/PCDHA3/PCDHGA1/CDH1/CDH12/CELSR1/CDH19/PCDHGA3/CDH23/CDH18/CDHR5/DSC3/PCDHGA9/PCDHGA7/CNTN2/DSC2/DSG1/PCDHGA8/CADM2/RET
## GO:0042273 RPL5/NPM1/RPL26/RPL35/RPL7/RPL6/RPL10A/RPL11/RPL7A/RPLP0/RPL14/RPL35A/NOP53/RRS1/GTPBP4/RPL38/NIP7/RPL24/RPF2/RSL1D1/MRTO4/NOP16/WDR12/MAK16/RPL23A/BOP1/GTF3A/PPAN/TMA16/MDN1/RPL10
## GO:0098742 ROBO1/BMP2/CNTN6/ROBO3/PCDHGC3/BSG/LRRC4B/ARVCF/IGSF21/PCDHGC4/PALLD/SLITRK1/PCDH1/PVR/PCDHB7/PCDH7/SELE/KIRREL3/AMIGO2/CDH4/PLXNB3/AMIGO1/ALCAM/TENM1/PCDHB8/CPLANE2/CADM4/NECTIN4/MMP24/NTNG1/DCHS1/CEACAM19/PCDHA2/FAT1/UNC5D/PCDHB6/PCDHA1/TENM4/PCDH9/PCDH11Y/PCDHGA2/PCDHGB6/CDHR3/MYOT/CXADR/PCDHGC5/CDHR1/SLITRK2/FAT4/PCDHA3/PCDHGA1/CDH1/CDH12/CLDN1/CELSR1/CDH19/MDGA1/PCDHGA3/CDH23/CDH18/CDHR5/DSC3/PCDHGA9/PCDHGA7/CNTN2/LRRC4/DSC2/DSG1/PCDHGA8/CADM2/SELP/RET
## GO:0042274 RPS7/NPM1/RPS6/RPS8/RPS17/RPS19/RPS27/RPS25/RPS14/RRS1/RPS24/RPL38/RPP40/RPS5/NOB1/RPS21/RPS16/RPS28/WDR3/RPSA/NOP14/UTP20/RPS15/WDR43/BYSL/RCL1/DCAF13/PRKDC/PWP2
## GO:0000027 RPL5/RPL6/RPL11/RPLP0/NOP53/RRS1/RPL38/RPL24/RPF2/MRTO4/RPL23A/BOP1/PPAN/MDN1/RPL10
## GO:0042254 RPS7/RPL5/NPM1/GPATCH4/RPL26/RPS6/RPS8/RPL35/RPL7/RPL6/RPL10A/RPS17/RPL11/RPL7A/RPLP0/FBL/RPS19/RPL14/DDX21/RPS27/RPL35A/RPS25/RPS14/NOP53/RRS1/LYAR/GTPBP4/RPS24/RPL38/GNL2/EIF2A/NPM3/RPP40/RPS5/NOB1/NIP7/RPL24/RPS21/RPF2/RSL1D1/RPL27/MRTO4/NOP16/RPS16/RPS28/WDR3/UTP14A/RPSA/WDR12/MAK16/NOP14/PA2G4/UTP20/RPS15/WDR43/BYSL/RPL23A/ABCE1/BOP1/GTF3A/RRP9/PPAN/RCL1/DDX10/EXOSC4/DCAF13/TMA16/MDN1/PRKDC/C1QBP/EXOSC5/RPL10/ERI1/GLUL/PWP2
## GO:0034765 CACNA1A/LRRC38/NALCN/GSG1L/FXYD6/PDE4B/KCNQ3/KCNIP1/TMC2/CABP2/CLIC4/FGF14/KCNJ5/KCNA1/KCNG1/RGN/RASGRF1/CRACR2A/PHB2/F2/HECW2/FXYD2/DPP6/NLGN3/SCN4B/CACNG7/TMC1/KCNJ9/CLCNKB/KCNK12/WNK3/SNCA/GSTO1/CACNG4/TESC/ABCC8/JPH1/HECW1/KCNA5/SLC43A1/JSRP1/KCNH7/TRPC3/KCNJ10/KCNA2/CASQ1/ANTKMT/P2RX3/CFTR/NEDD4/GPD1L/AMIGO1/KCNK10/ARC/SCN2B/LRRC26/PLP1/UBASH3B/CLCN1/KEL/SCN1B/FKBP1B/CAV3/NOS1/AGT/EDN1/KCNJ8/NTSR1/KCNK17/SHISA7/KCNJ16/HCN4/CEMIP/AKAP5/KCNIP3/CLIC1/PPP3R2/CACNA1B/KCNE5/PIRT/PDE4D/KCNJ1/APLNR/ADCYAP1R1/ATP1A2/CACNA2D2/KCNC1/KCNE3/KCNJ3/SHANK1/KCND3/BDKRB1/STOM/ADRA2A/ARG1/KCNJ12/CNKSR3/CHRM3/CNIH2/KCNJ15/CACNA1G/CACNB3/SLC26A5/DPP10/LRRC55/KCNIP4/CAV1/EDN3/MMP9
## GO:0050804 CACNA1A/NALCN/SLC6A4/GRIN2C/ERC1/RAPSN/GRID2IP/NEURL1/NTF3/MAPT/RPL22/FRRS1L/RASGRF1/SYT11/CDC20/DISC1/ROR2/NLGN3/GFAP/CACNG7/IQSEC2/PRKCG/CAMK2A/DCC/CSPG5/SNCAIP/CD38/SNCA/CRTC1/CACNG4/SRC/SYT12/ATF4/SYT4/CHRNA5/SLC4A8/KCNJ10/TNR/GRM4/P2RX3/SCGN/STXBP1/DGKI/GRM8/GABBR1/SLC12A2/FAM107A/NRG3/ARC/NEUROD2/NTNG1/AGT/EDN1/PTN/GRIN3A/CELF4/SHISA7/GRIK3/WNT7A/ADORA1/AKAP5/PRKCB/CACNA1B/NSG1/OXTR/CYP46A1/ITPKA/SORCS2/BCHE/GRM7/ATP1A2/ACHE/ASIC1/CDH1/SORCS3/SHANK1/BAIAP2/SLC24A2/NPTX2/ADRA2A/CNR2/SLC1A1/NTRK2/GLUL/SLC7A11/CNIH2/APBA1/CNTN2/LRRC4/PHF24/TMEM108/GRM1/P2RY1/TACR1/SYT1/GRIK4
## GO:0099177 CACNA1A/NALCN/SLC6A4/GRIN2C/ERC1/RAPSN/GRID2IP/NEURL1/NTF3/MAPT/RPL22/FRRS1L/RASGRF1/SYT11/CDC20/DISC1/ROR2/NLGN3/GFAP/CACNG7/IQSEC2/PRKCG/CAMK2A/DCC/CSPG5/SNCAIP/CD38/SNCA/CRTC1/CACNG4/SRC/SYT12/ATF4/SYT4/CHRNA5/SLC4A8/KCNJ10/TNR/GRM4/P2RX3/SCGN/STXBP1/DGKI/GRM8/GABBR1/SLC12A2/FAM107A/NRG3/ARC/NEUROD2/NTNG1/AGT/EDN1/PTN/GRIN3A/CELF4/SHISA7/GRIK3/WNT7A/ADORA1/AKAP5/PRKCB/CACNA1B/NSG1/OXTR/CYP46A1/ITPKA/SORCS2/BCHE/GRM7/ATP1A2/ACHE/ASIC1/CDH1/SORCS3/SHANK1/BAIAP2/SLC24A2/NPTX2/ADRA2A/CNR2/SLC1A1/NTRK2/GLUL/SLC7A11/CNIH2/APBA1/CNTN2/LRRC4/PHF24/TMEM108/GRM1/P2RY1/TACR1/SYT1/GRIK4
## GO:0098657 CACNA1A/SLC6A4/SLC39A4/SLC29A2/SLC1A6/KCNJ5/SLC7A2/SCNN1B/FXYD2/GFAP/GDNF/SLC9A2/KCNJ9/SLC6A12/EPRS1/WNK3/SNCA/ABCC8/SLC7A3/SLC43A1/SLC22A4/SCNN1G/KCNJ10/SLC29A1/SLC6A3/SLC9A5/SLC18A2/SLC12A2/SLC39A14/NOS1/TRPV5/LRRC8E/AGT/SLC12A8/KCNJ8/NTSR1/KCNJ16/HCN4/AKAP5/PPP3R2/CACNA1B/TRPM1/MAPK15/KCNJ1/CNGA3/ATP1A2/KCNJ3/STEAP4/GPM6B/TRPV3/SLC24A2/ARG1/KCNJ12/SLC1A1/SLC9C1/SLC7A11/KCNJ15/TFR2/GRM1/SLC12A5/SLC6A13
## GO:0061564 PAX6/ROBO1/NTN5/NTN3/KIAA0319/CHL1/RGMA/GDF7/PLXNC1/CNTN6/ROBO3/ULK2/FEZ2/MAPT/RND2/RPL24/BSG/SEMA4D/VCL/MAP6/DISC1/EPHA7/NLGN3/VASP/HSP90AB1/GDNF/NOVA2/FEZF2/DCC/PALLD/CSPG5/SLITRK1/LHX1/ISL1/KALRN/PAK3/POU3F2/NOTCH2/ATP8A2/TRIO/BARHL2/CCKAR/EPHB4/EPHB3/DLX5/NKX6-1/CDK5R1/TNR/INPP5F/MAP2/MTR/CDH4/PLXNB3/STXBP1/AMIGO1/EPHA5/ALCAM/PTPRO/LMX1A/TSPO/KIF5A/SLIT1/PLP1/KEL/SLIT3/SCN1B/FKBP1B/RHOH/TSPAN2/DCLK1/NTNG1/LAMC2/EDN1/BCL11B/PTN/UNC5D/MT3/WNT7A/EPHA2/NEFL/LHX9/FN1/MYOT/GRM7/NRCAM/PARD6B/EDN2/SLITRK2/LHX4/LGR6/NEFH/CDH1/BAIAP2/TIAM1/RTN4RL1/NTRK2/NR4A2/VASH2/VAX1/CNTN2/SMO/SEMA3E/EDN3/RET
## GO:0050808 TPBG/SPOCK2/ERC1/RAPSN/PPFIA4/NEURL1/RAB39B/PCDHGC3/MAPT/BCAN/SEMA4D/FRRS1L/LRRC4B/GABRB3/NRXN2/CDC20/DISC1/EPHA7/IGSF21/NLGN3/GDNF/PPFIBP2/PCDHGC4/SLITRK1/ELFN1/CTTNBP2/PAK3/FZD5/SNCA/COL4A5/EPHB3/CDK5R1/TNR/NTRK3/KIRREL3/AMIGO2/CHRNA1/NEDD4/AMIGO1/LGI2/LZTS3/PTPRO/LMX1A/GPM6A/ARC/SLIT1/NEUROD2/SRCIN1/CBLN4/NTNG1/WASF1/ACTG1/SHISA7/SNCB/PPFIBP1/GLRB/WNT7A/PCDHB6/PPFIA2/C1QL3/NEFL/OXTR/MUSK/CAMKV/ITPKA/DNM3/MYOT/PRMT3/LRRTM3/C5AR1/NRCAM/PCDHGC5/ACHE/SLITRK2/FRMPD4/NEFH/CDH1/SHANK1/BAIAP2/MDGA1/TNNI3K/SLC1A1/NTRK2/TANC2/SLC7A11/COL4A1/CNKSR2/CACNB3/CNTN2/LRRC4/SEMA3E/TMEM108/C1QL1
## GO:0006364 RPS7/RPL5/RPL26/RPS6/RPS8/RPL35/RPL7/RPL10A/RPS17/RPL11/RPL7A/FBL/RPS19/RPL14/DDX21/RPS27/RPL35A/RPS25/RPS14/NOP53/RRS1/LYAR/GTPBP4/RPS24/NPM3/RPP40/NOB1/RPS21/RPF2/RSL1D1/RPL27/MRTO4/RPS16/RPS28/WDR3/UTP14A/WDR12/MAK16/NOP14/PA2G4/UTP20/RPS15/WDR43/BYSL/BOP1/RRP9/PPAN/RCL1/DDX10/EXOSC4/DCAF13/MDN1/PRKDC/EXOSC5/ERI1/PWP2
## GO:0071805 LRRC38/NALCN/KCNQ3/KCNIP1/KCNJ5/KCNA1/KCNG1/FXYD2/DPP6/SLC9A2/KCNJ9/KCNK12/WNK3/ABCC8/KCNA5/KCNH7/KCNJ10/KCNA2/SLC9A5/NEDD4/AMIGO1/HPN/KCNK10/SLC12A2/LRRC26/KEL/CAV3/SLC12A8/KCNJ8/KCNK17/KCNJ16/KCNT2/HCN4/KCNIP3/KCNT1/KCNE5/KCNJ1/ATP1A2/KCNC1/KCNE3/KCNJ3/KCND3/SLC24A2/KCNJ12/SLC9C1/KCNJ15/SLC24A5/DPP10/LRRC55/SLC12A5/KCNIP4/CAV1/EDN3
## GO:0097485 PAX6/ROBO1/NTN5/NTN3/CHL1/GDF7/PLXNC1/CNTN6/ROBO3/FEZ2/RPL24/BSG/SEMA4D/DPYSL4/EPHA7/VASP/GDNF/NOVA2/FEZF2/DCC/PALLD/LHX1/ISL1/KALRN/NOTCH2/TRIO/EPHB4/EPHB3/DLX5/CDK5R1/TNR/CDH4/PLXNB3/EPHA5/ALCAM/PTPRO/LMX1A/KIF5A/SLIT1/SLIT3/SCN1B/RHOH/LAMC2/EDN1/BCL11B/UNC5D/EPHA2/LHX9/MYOT/NRCAM/LHX4/LGR6/VAX1/CNTN2/SMO/SEMA3E/EDN3/RET
## GO:0001764 PAX6/KIAA0319/CHL1/NEXMIF/ADGRG1/FGFR1/DISC1/EMX2/FEZF2/CAMK2A/DCC/LHX1/ASTN2/BARHL2/CCKAR/SPOCK1/NKX6-1/ZMIZ1/OLIG3/CDK5R1/NTRK3/KIRREL3/NAV1/GPM6A/NRG3/DDIT4/TUBB2A/DCLK1/NTNG1/UNC5D/PHACTR1/SH3RF1/ASTN1/NDNF/NRCAM/CELSR1/MDGA1/WDR62/NTRK2/NR4A2/VAX1/CXCR4/ULK4/CNTN2/DCX/SEMA3E/EOMES
## GO:0006813 LRRC38/NALCN/KCNQ3/KCNIP1/KCNJ5/KCNA1/KCNG1/FXYD2/DPP6/SLC9A2/KCNJ9/KCNK12/WNK3/ATF4/ABCC8/KCNA5/KCNH7/KCNJ10/KCNA2/SLC9A5/NEDD4/AMIGO1/HPN/KCNK10/SLC12A2/LRRC26/KEL/CAV3/NOS1/SLC12A8/KCNJ8/KCNK17/KCNJ16/KCNT2/HCN4/ADORA1/KCNIP3/KCNT1/KCNE5/KCNJ1/ATP1A2/KCNC1/KCNE3/KCNJ3/KCND3/SLC24A2/ADRA2A/KCNJ12/SLC9C1/KCNJ15/SLC24A5/DPP10/LRRC55/SLC12A5/KCNIP4/CAV1/EDN3
## GO:0050878 NPR3/RPLP0/NEURL1/SLC29A2/AQP5/PDGFA/AQP6/VCL/SCNN1B/APRT/P2RY12/DGKG/F2/GHRHR/FOXA2/PLSCR1/TFAP2B/SRC/SCNN1G/SYK/SLC29A1/SLC6A3/HSPB1/CFTR/STXBP1/DGKI/ABAT/PTPRO/DGKK/DGKB/UBASH3B/TRPV5/EDN1/ANXA2/MFSD2B/TFPI2/ST3GAL4/ACTG1/ADORA1/ZBTB7B/CLIC1/MPL/GRHL3/HGFAC/EMP2/OXTR/FN1/APLNR/PRSS56/NEUROG1/FGF10/TRAF3IP2/GRHL1/SFN/CDO1/ITGA2/KLKB1/CLDN1/EMILIN1/SERPING1/ADRA2A/ENTPD2/CYP4F12/SCUBE1/PAM/CHRM3/CYP4F11/SLC7A11/ADAMTS18/STAT5A/MYL9/ALOX12B/SLC4A1/DGKH/P2RY1/TACR1/SELP/CAV1/F2RL1/HK2/FZD6
## GO:0016072 RPS7/SLFN13/RPL5/RPL26/RPS6/RPS8/RPL35/RPL7/RPL10A/RPS17/RPL11/RPL7A/FBL/RPS19/RPL14/DDX21/RPS27/RPL35A/RPS25/RPS14/NOP53/RRS1/LYAR/GTPBP4/RPS24/NPM3/RPP40/NOB1/MAPT/RPS21/RPF2/NCL/RSL1D1/RPL27/MRTO4/RPS16/RPS28/WDR3/UTP14A/WDR12/MAK16/NOP14/PA2G4/UTP20/RPS15/WDR43/BYSL/BOP1/GTF3A/RRP9/PPAN/RCL1/TP53/DDX10/EXOSC4/DCAF13/MDN1/PRKDC/POLR1E/EXOSC5/ERI1/PWP2
## GO:0007411 PAX6/ROBO1/NTN5/NTN3/CHL1/GDF7/PLXNC1/CNTN6/ROBO3/FEZ2/RPL24/BSG/SEMA4D/EPHA7/VASP/GDNF/NOVA2/FEZF2/DCC/PALLD/LHX1/ISL1/KALRN/NOTCH2/TRIO/EPHB4/EPHB3/DLX5/CDK5R1/TNR/CDH4/PLXNB3/EPHA5/ALCAM/PTPRO/LMX1A/KIF5A/SLIT1/SLIT3/SCN1B/RHOH/LAMC2/EDN1/BCL11B/UNC5D/EPHA2/LHX9/MYOT/NRCAM/LHX4/LGR6/VAX1/CNTN2/SMO/SEMA3E/EDN3/RET
## GO:0007409 PAX6/ROBO1/NTN5/NTN3/KIAA0319/CHL1/GDF7/PLXNC1/CNTN6/ROBO3/ULK2/FEZ2/MAPT/RND2/RPL24/BSG/SEMA4D/VCL/MAP6/DISC1/EPHA7/NLGN3/VASP/HSP90AB1/GDNF/NOVA2/FEZF2/DCC/PALLD/SLITRK1/LHX1/ISL1/KALRN/PAK3/POU3F2/NOTCH2/ATP8A2/TRIO/BARHL2/CCKAR/EPHB4/EPHB3/DLX5/NKX6-1/CDK5R1/TNR/MAP2/CDH4/PLXNB3/STXBP1/AMIGO1/EPHA5/ALCAM/PTPRO/LMX1A/KIF5A/SLIT1/KEL/SLIT3/SCN1B/RHOH/DCLK1/NTNG1/LAMC2/EDN1/BCL11B/UNC5D/MT3/WNT7A/EPHA2/NEFL/LHX9/FN1/MYOT/NRCAM/PARD6B/EDN2/SLITRK2/LHX4/LGR6/NEFH/CDH1/BAIAP2/TIAM1/NTRK2/NR4A2/VAX1/CNTN2/SMO/SEMA3E/EDN3/RET
## GO:0006836 SLC6A4/SV2B/RPH3A/SLC29A2/SLC1A6/SLC6A7/NRXN2/SYT11/GFAP/GDNF/PRKCG/CAMK2A/CSPG5/SNCAIP/SLC6A12/SYT5/CADPS2/SNCA/SYT12/SYT4/CHRNA5/SLC4A8/KCNJ10/SLC29A1/SLC6A3/SLC18A2/GRM4/STXBP1/DOC2B/RPH3AL/LIN7A/NOS1/GRIN3A/SV2A/WNT7A/PTPRN2/OTOF/PRKCB/CADPS/ATP1A2/GPM6B/ASIC1/ICA1/ADRA2A/SLC1A1/SYT2/APBA1/SLC6A8/P2RY1/SLC18A3/SLC6A13/SYT1
## GO:0043270 LRRC38/ATP2C2/SLC6A4/KCNA1/P2RY12/RGN/CRACR2A/F2/FXYD2/NLGN3/SCN4B/GDNF/CAMK2A/WNK3/SNCA/GSTO1/CACNG4/TESC/ATF4/ABCC8/SYT4/TRPC3/CASQ1/ANTKMT/STC1/P2RX3/CFTR/STXBP1/GPD1L/AMIGO1/TNFRSF11A/ABAT/GABBR1/SLC12A2/TSPO/ARC/LRRC26/PLP1/SCN1B/NOS1/AGT/EDN1/NTSR1/CEMIP/ADORA1/AKAP5/PPP3R2/KCNE5/PIRT/OXTR/APLNR/ADCYAP1R1/NKX2-5/KCNC1/BDKRB1/TRPV3/ADRA2A/JAK3/CNKSR3/CACNB3/LRRC55/P2RY1/CAV1/EDN3/SYT1
## GO:0030900 PAX6/ROBO1/DCT/BMP2/LHX5/PITX1/GDF7/KCNA1/HDAC1/ADGRG1/BCAN/P2RY12/ATIC/BMERB1/DISC1/GHRHR/EMX2/FEZF2/IGF2BP1/GLI1/LHX1/ISL1/POU3F2/CCDC85C/CCKAR/DCLK2/SRC/PITX2/EPHB3/CCDC141/DLX5/ZMIZ1/TAL2/SLC6A3/CDK5R1/TNR/KIRREL3/ATF5/INHBA/EPHA5/HTR5A/POU3F1/LMX1A/NRG3/SLIT1/DNAJB1/DCLK1/PGAP1/BCL11B/DLX2/CHD5/PHACTR1/WNT7A/NEFL/ZIC1/NDNF/OXTR/ATP1A2/KCNC1/H2AX/FGF10/FAT4/KIF14/CDH1/RAC3/MDGA1/WDR62/RTN4RL1/NTRK2/NEUROD6/SLC7A11/NR4A2/ARHGAP11B/CXCR4/CNTN2/SMO/SEMA3E/SALL1/TMEM108/EOMES/HES5/NHLH2
## GO:2000027 ROBO1/BMP2/PDGFA/DACT1/PHB2/ROR2/GDNF/RSPO2/LHX1/SOX8/WNT9B/JHY/APCDD1/SAPCD2/CAV3/AGT/EDN1/TNFRSF11B/WNT7A/GRHL3/PRICKLE1/FGF7/ROR1/ARHGEF19/CITED2/FGF10/RSPO3/TIAM1/CELSR1/FZD7/WNT10A/SPEF1/SMO/NTN4/SNAI2/FZD6
## GO:0000028 RPS19/RPS27/RPS14/RPL38/RPS5/RPS28/RPSA/RPS15/PRKDC/PWP2
## GO:0042391 NALCN/GRIN2C/RACK1/KCNQ3/SLC1A6/KCNJ5/KCNA1/MAPT/GABRA5/SLC4A4/GABRB3/OPRD1/RGS7BP/NLGN3/SCN4B/KCNK12/SNCA/CRTC1/SRC/KCNA5/PYCR1/INSYN2A/CHRNA5/KCNH7/SLC4A8/KCNJ10/SLC29A1/KCNA2/SLC4A3/CHRNA1/P2RX3/CFTR/NEDD4/GPD1L/GABRR2/KCNK10/ABAT/GABBR1/TSPO/CHRND/SCN2B/CLCN1/SCN1B/FKBP1B/CAV3/GLRA2/EDN1/KCNJ8/GRIN3A/CELF4/NTSR1/KCNK17/GRIK3/HCN4/GLRB/WNT7A/ADORA1/GABRB1/CLIC1/KCNE5/ATP1A2/KCNC1/CXADR/NRCAM/KCNE3/KCNJ3/BVES/GABRG3/ASIC1/NTSR2/SHANK1/BAIAP2/KCND3/CNR2/NTRK2/MYOC/CNIH2/BEST2/CACNA1G/CACNB3/DSC2/SLC26A5/TMEM108/GRM1/PMAIP1/TACR1/CAV1/GRIK4
## GO:1904062 LRRC38/GSG1L/FXYD6/PDE4B/KCNIP1/TMC2/CABP2/FGF14/KCNA1/KCNG1/RGN/RASGRF1/CRACR2A/PHB2/F2/HECW2/FXYD2/DPP6/NLGN3/SCN4B/CACNG7/TMC1/WNK3/SNCA/GSTO1/CACNG4/TESC/ABCC8/JPH1/HECW1/SLC43A1/JSRP1/TRPC3/CASQ1/ANTKMT/P2RX3/NEDD4/GPD1L/AMIGO1/ARC/SCN2B/LRRC26/PLP1/UBASH3B/KEL/SCN1B/FKBP1B/CAV3/NOS1/AGT/EDN1/NTSR1/SHISA7/HCN4/CEMIP/AKAP5/KCNIP3/PPP3R2/KCNE5/PIRT/PDE4D/APLNR/ADCYAP1R1/ATP1A2/KCNC1/KCNE3/SHANK1/BDKRB1/STOM/ADRA2A/ARG1/CNKSR3/CNIH2/CACNB3/DPP10/LRRC55/KCNIP4/CAV1/EDN3/MMP9
## GO:0048880 PAX6/PLAAT1/PBX3/PBX1/IMPDH2/AQP5/PKNOX1/CLIC4/HDAC1/RPL24/RPGRIP1/P2RY12/SALL2/LHX1/PROM1/ISL1/COL8A1/MEIS3/SOX8/NOTCH2/FZD5/ATP8A2/TULP1/BARHL2/TFAP2B/ATF4/PITX2/TGIF2/WNT9B/SLC6A3/IFT122/INHBA/TMEM231/SMARCD3/FBN2/GPM6A/SIX5/SIX6/FREM2/CRYBG3/PRPH2/SCO2/BCL11B/DLX2/CELF4/OSR2/FAT1/FZD4/FGF9/WNT7A/USP45/LRP5L/GRHL3/RCN1/EPHA2/SH3PXD2B/TMOD1/CRYGN/PRSS56/BFSP2/CITED2/FGF10/ACHE/LRP5/EFEMP1/SERPINF1/SPRED3/SLC1A1/NTRK2/SLC7A11/ADAMTS18/COL5A1/COL4A1/COL5A2/WNT16/DLL1/VAX1/DCX/SLC44A4/HES5/GNGT1/UNC45B/CPAMD8/RET
## GO:0098659 CACNA1A/SLC39A4/KCNJ5/SCNN1B/FXYD2/SLC9A2/KCNJ9/WNK3/ABCC8/SCNN1G/KCNJ10/SLC9A5/SLC12A2/SLC39A14/TRPV5/SLC12A8/KCNJ8/KCNJ16/HCN4/AKAP5/PPP3R2/CACNA1B/TRPM1/KCNJ1/CNGA3/ATP1A2/KCNJ3/TRPV3/SLC24A2/KCNJ12/SLC9C1/KCNJ15/SLC12A5
## GO:0099587 CACNA1A/SLC39A4/KCNJ5/SCNN1B/FXYD2/SLC9A2/KCNJ9/WNK3/ABCC8/SCNN1G/KCNJ10/SLC9A5/SLC12A2/SLC39A14/TRPV5/SLC12A8/KCNJ8/KCNJ16/HCN4/AKAP5/PPP3R2/CACNA1B/TRPM1/KCNJ1/CNGA3/ATP1A2/KCNJ3/TRPV3/SLC24A2/KCNJ12/SLC9C1/KCNJ15/SLC12A5
## GO:0021953 PAX6/LHX5/MNX1/GDF7/MAPT/FAIM2/DISC1/HSP90AB1/NOVA2/FEZF2/DCC/LHX1/ISL1/LBX1/HOXC10/DCLK2/SPOCK1/EPHB3/NKX6-1/ZMIZ1/OLIG3/MAP2/INHBA/LMX1A/SCN1B/DCLK1/SOX4/BCL11B/DLX2/CHD5/WNT7A/GABRB1/NDNF/LHX4/RAC3/MDGA1/NTRK2/NR4A2/CNTN2/SMO/SEMA3E/EOMES/HES5/NHLH2
## GO:0007589 NPR3/RPLP0/NEURL1/SLC29A2/AQP5/SCNN1B/APRT/GHRHR/SLC29A1/SLC6A3/EDN1/ADORA1/ZBTB7B/OXTR/NEUROG1/FGF10/TRAF3IP2/CDO1/PAM/CHRM3/STAT5A/ALOX12B/TACR1/CAV1/HK2
## GO:0001505 SLC6A4/RPH3A/SLC29A2/SLC1A6/NRXN2/SYT11/GFAP/GDNF/PRKCG/CAMK2A/CSPG5/SNCAIP/SLC6A12/PDE1B/SYT5/CADPS2/SNCA/SYT12/SYT4/CHRNA5/SLC4A8/KCNJ10/SLC29A1/SLC6A3/SLC18A2/GRM4/STXBP1/ABAT/DOC2B/RPH3AL/LIN7A/NOS1/GRIN3A/SV2A/WNT7A/PTPRN2/OTOF/PRKCB/MAOB/BCHE/CADPS/ATP1A2/ACHE/GPM6B/ASIC1/ADRA2A/SYT2/APBA1/SLC44A4/P2RY1/SLC6A13/SYT1
## GO:0150063 PAX6/PLAAT1/PBX3/PBX1/IMPDH2/AQP5/PKNOX1/CLIC4/HDAC1/RPL24/RPGRIP1/P2RY12/SALL2/LHX1/PROM1/COL8A1/MEIS3/SOX8/NOTCH2/FZD5/ATP8A2/TULP1/BARHL2/TFAP2B/ATF4/PITX2/TGIF2/WNT9B/SLC6A3/IFT122/INHBA/TMEM231/SMARCD3/FBN2/GPM6A/SIX5/SIX6/FREM2/CRYBG3/PRPH2/SCO2/BCL11B/DLX2/CELF4/OSR2/FAT1/FZD4/FGF9/WNT7A/USP45/LRP5L/GRHL3/RCN1/EPHA2/SH3PXD2B/TMOD1/CRYGN/PRSS56/BFSP2/CITED2/FGF10/ACHE/LRP5/EFEMP1/SERPINF1/SPRED3/SLC1A1/NTRK2/SLC7A11/ADAMTS18/COL5A1/COL4A1/COL5A2/WNT16/DLL1/VAX1/DCX/HES5/GNGT1/UNC45B/CPAMD8/RET
## GO:1904667 RPS7/RPL5/RPL23/RPL11/RPS20/RPL37/RPS15/MAD2L1
## GO:0098739 CACNA1A/SLC39A4/SLC1A6/KCNJ5/SLC7A2/SCNN1B/FXYD2/GFAP/SLC9A2/KCNJ9/WNK3/ABCC8/SLC7A3/SLC43A1/SLC22A4/SCNN1G/KCNJ10/SLC9A5/SLC12A2/SLC39A14/TRPV5/LRRC8E/AGT/SLC12A8/KCNJ8/NTSR1/KCNJ16/HCN4/AKAP5/PPP3R2/CACNA1B/TRPM1/KCNJ1/CNGA3/ATP1A2/KCNJ3/TRPV3/SLC24A2/ARG1/KCNJ12/SLC1A1/SLC9C1/SLC7A11/KCNJ15/GRM1/SLC12A5/SLC6A13
## GO:0006936 PDE4B/KCNJ5/KCNA1/SCNN1B/GAMT/TTN/SCN4B/GDNF/ENO1/CD38/ATP8A2/GSTO1/KCNA5/PROK2/DES/JSRP1/CASQ1/CHRNA1/STC1/P2RX3/GPD1L/ABAT/SULF2/CHRND/GRIP2/TNNT2/SCN2B/CLCN1/CCDC78/SCN1B/FKBP1B/CAV3/NOS1/AGT/EDN1/KCNJ8/HCN4/ADORA1/KCNE5/TNNT3/OXTR/PDE4D/TMOD1/SPHK1/MYOT/NKX2-5/ATP1A2/NEUROG1/KCNE3/KCNJ3/EDN2/ITGA2/BDKRB2/KCND3/MYH7/ADRA2A/ACTA1/TNNI3K/KCNJ12/CHRM3/CACNA1G/MYL9/ADRA1B/SLC6A8/CXCR4/ACTA2/DSC2/MYH11/MYL1/PGAM2/TACR1/CAV1/EDN3
## GO:0001654 PAX6/PLAAT1/PBX3/PBX1/IMPDH2/AQP5/PKNOX1/CLIC4/HDAC1/RPL24/RPGRIP1/SALL2/LHX1/PROM1/COL8A1/MEIS3/SOX8/NOTCH2/FZD5/ATP8A2/TULP1/BARHL2/TFAP2B/ATF4/PITX2/TGIF2/WNT9B/SLC6A3/IFT122/INHBA/TMEM231/SMARCD3/FBN2/GPM6A/SIX5/SIX6/FREM2/CRYBG3/PRPH2/SCO2/BCL11B/DLX2/CELF4/OSR2/FAT1/FZD4/FGF9/WNT7A/USP45/LRP5L/GRHL3/RCN1/EPHA2/SH3PXD2B/TMOD1/CRYGN/PRSS56/BFSP2/CITED2/FGF10/ACHE/LRP5/EFEMP1/SERPINF1/SPRED3/SLC1A1/NTRK2/SLC7A11/ADAMTS18/COL5A1/COL4A1/COL5A2/WNT16/DLL1/VAX1/DCX/HES5/GNGT1/UNC45B/CPAMD8/RET
## GO:0032412 LRRC38/GSG1L/FXYD6/PDE4B/CABP2/FGF14/KCNA1/KCNG1/RGN/RASGRF1/CRACR2A/PHB2/HECW2/FXYD2/NLGN3/SCN4B/CACNG7/WNK3/GSTO1/CACNG4/TESC/ABCC8/JPH1/HECW1/JSRP1/CASQ1/ANTKMT/CFTR/NEDD4/GPD1L/AMIGO1/ARC/SCN2B/LRRC26/SCN1B/FKBP1B/CAV3/NOS1/EDN1/NTSR1/SHISA7/HCN4/AKAP5/PPP3R2/KCNE5/PIRT/PDE4D/ATP1A2/KCNC1/KCNE3/SHANK1/STOM/ADRA2A/CNKSR3/CHRM3/CNIH2/CACNB3/LRRC55/CAV1/MMP9
## GO:0006865 SLC1A6/SLC6A7/SLC7A2/GFAP/SLC6A12/SNCA/SFXN5/SYT4/SLC7A3/SLC43A1/SLC22A4/KCNJ10/STXBP1/ABAT/GABBR1/SLC12A2/LRRC8E/AGT/SV2A/OCA2/NTSR1/ADORA1/SFXN2/GRM7/ATP1A2/SLC38A5/SLC7A10/ARG1/SLC1A1/NTRK2/SLC3A1/SLC7A11/APBA1/SLC66A1L/GRM1/SLC6A13/SLC7A14
## GO:0022613 RPS7/RPL5/NPM1/GPATCH4/RPL26/RPS6/RPS8/RPL35/RPL7/RPL6/EIF3E/EIF4B/RPL10A/RPS17/RPL11/RPL13A/RPL7A/RPLP0/FBL/RPS19/RPL14/DDX21/RPS27/RPL35A/RPS25/RPS14/NOP53/RRS1/LYAR/GTPBP4/RPS24/EIF2S3/RPL38/EIF3H/EIF3A/GNL2/EIF2A/NPM3/RPP40/RPS5/NOB1/NIP7/RPL24/CELF5/RPS21/RPF2/RSL1D1/RPL27/MRTO4/EIF3D/NOP16/RPS16/RPS28/SNRPD2/WDR3/UTP14A/HSP90AB1/RPSA/WDR12/MAK16/NOP14/PA2G4/RUVBL1/UTP20/RPS15/WDR43/BYSL/RPL23A/ABCE1/BOP1/GTF3A/EIF2D/RRP9/PPAN/RCL1/EIF3M/DDX10/EXOSC4/DCAF13/TMA16/MDN1/PRKDC/C1QBP/EXOSC5/SNRPB/RPL10/CELF4/ERI1/EIF3C/GLUL/PWP2
## GO:0022898 LRRC38/GSG1L/FXYD6/PDE4B/CABP2/FGF14/KCNA1/KCNG1/RGN/RASGRF1/CRACR2A/PHB2/HECW2/FXYD2/NLGN3/SCN4B/CACNG7/WNK3/SNCA/GSTO1/CACNG4/TESC/ABCC8/JPH1/HECW1/JSRP1/CASQ1/ANTKMT/CFTR/NEDD4/GPD1L/AMIGO1/ARC/SCN2B/LRRC26/SCN1B/FKBP1B/CAV3/NOS1/EDN1/NTSR1/SHISA7/HCN4/AKAP5/PPP3R2/KCNE5/PIRT/PDE4D/ATP1A2/KCNC1/KCNE3/SHANK1/STOM/ADRA2A/CNKSR3/CHRM3/CNIH2/CACNB3/LRRC55/CAV1/MMP9
## GO:0030198 MFAP4/LOXL4/BMP2/SPOCK2/SPINK5/COL4A6/COL9A2/COL19A1/CRTAP/PRDX4/COL13A1/NPHS1/COL11A2/COL24A1/GFAP/COL14A1/ADAMTS14/COL8A1/LRP1/FERMT1/COL4A5/NID2/CCDC80/TNR/OLFML2B/ADAMTS2/HPN/MMP21/SULF2/CTSV/POSTN/CARMIL2/MMP24/NTNG1/AGT/ANXA2/ADAMTS19/MMP25/TNFRSF11B/SERPINH1/ADAMTS7/SH3PXD2B/NDNF/MMP23B/LOXL1/ITGA8/ADAMTS9/GPM6B/TGFB1/EMILIN1/ADAMTS8/ADAMTS18/CREB3L1/COL5A1/COL4A1/COL5A2/MATN1/RXFP1/NTN4/SMOC2/MYH11/HAPLN2/CAV1/MMP9/GAS2/MYO1E
## GO:0098656 SLC1A6/CLIC4/AQP6/GABRA5/SLC25A19/SLC7A2/SLC4A4/GABRB3/SLC25A5/ABCC4/CLCN4/SLC5A12/GFAP/SLC37A1/CLCNKB/BEST1/SLC4A2/SLC7A3/SLC43A1/SLC4A8/KCNJ10/SLC4A3/CFTR/GABRR2/SLC12A2/CLCN1/SLC26A11/LRRC8E/AGT/SLC25A32/GLRA2/SLC12A8/NTSR1/GLRB/GABRB1/CLIC1/SFXN2/ATP1A2/SLC38A5/GABRG3/SLC26A4/ADAMTS8/ARG1/SLC26A9/SLC1A1/SLC3A1/SLC7A11/BEST2/SLC66A1L/SLC6A8/SLC26A5/SLC4A1/GRM1/SLC44A4/TTYH1/SLC12A5/SLC6A13/SLC13A4
## GO:0043062 MFAP4/LOXL4/BMP2/SPOCK2/SPINK5/COL4A6/COL9A2/COL19A1/CRTAP/PRDX4/COL13A1/NPHS1/COL11A2/COL24A1/GFAP/COL14A1/ADAMTS14/COL8A1/LRP1/FERMT1/COL4A5/NID2/CCDC80/TNR/OLFML2B/ADAMTS2/HPN/MMP21/SULF2/CTSV/POSTN/CARMIL2/MMP24/NTNG1/AGT/ANXA2/ADAMTS19/MMP25/TNFRSF11B/SERPINH1/ADAMTS7/SH3PXD2B/NDNF/MMP23B/LOXL1/ITGA8/ADAMTS9/GPM6B/TGFB1/EMILIN1/ADAMTS8/ADAMTS18/CREB3L1/COL5A1/COL4A1/COL5A2/MATN1/RXFP1/NTN4/SMOC2/MYH11/HAPLN2/CAV1/MMP9/GAS2/MYO1E
## GO:0015701 SLC4A4/CYB5R2/BEST1/SLC4A2/SLC4A8/SLC4A3/CFTR/SLC26A4/CA4/SLC26A9/SLC26A5/SLC4A1
## GO:0045229 MFAP4/LOXL4/BMP2/SPOCK2/SPINK5/COL4A6/COL9A2/COL19A1/CRTAP/PRDX4/COL13A1/NPHS1/COL11A2/COL24A1/GFAP/COL14A1/ADAMTS14/COL8A1/LRP1/FERMT1/COL4A5/NID2/CCDC80/TNR/OLFML2B/ADAMTS2/HPN/MMP21/SULF2/CTSV/POSTN/CARMIL2/MMP24/NTNG1/AGT/ANXA2/ADAMTS19/MMP25/TNFRSF11B/SERPINH1/ADAMTS7/SH3PXD2B/NDNF/MMP23B/LOXL1/ITGA8/ADAMTS9/GPM6B/TGFB1/EMILIN1/ADAMTS8/ADAMTS18/CREB3L1/COL5A1/COL4A1/COL5A2/MATN1/RXFP1/NTN4/SMOC2/MYH11/HAPLN2/CAV1/MMP9/GAS2/MYO1E
## GO:0010959 LRRC38/ATP2C2/FXYD6/TF/PDE4B/KCNIP1/STC2/TMC2/CABP2/FGF14/KCNA1/KCNG1/RGN/OPRD1/CRACR2A/F2/HECW2/FXYD2/DPP6/SCN4B/CAMK2A/TMC1/WNK3/SNCA/GSTO1/BEST1/TESC/ATF4/ABCC8/JPH1/HECW1/KCNA5/JSRP1/TRPC3/CASQ1/CRACR2B/STC1/P2RX3/NEDD4/GPD1L/AMIGO1/PLPP4/TSPO/SCN2B/LRRC26/PLP1/UBASH3B/KEL/SCN1B/FKBP1B/CAV3/NOS1/AGT/NTSR1/CEMIP/ADORA1/AKAP5/KCNIP3/PPP3R2/KCNE5/PDE4D/APLNR/ADCYAP1R1/NKX2-5/ATP1A2/KCNC1/KCNE3/BDKRB1/TRPV3/STOM/ADRA2A/JAK3/CNKSR3/CACNB3/CXCR4/GRAMD2A/DPP10/LRRC55/KCNIP4/CAV1/EDN3
## GO:0021537 PAX6/ROBO1/BMP2/LHX5/KCNA1/HDAC1/ADGRG1/BCAN/P2RY12/ATIC/BMERB1/DISC1/EMX2/FEZF2/IGF2BP1/LHX1/POU3F2/CCDC85C/DCLK2/EPHB3/CCDC141/DLX5/ZMIZ1/CDK5R1/TNR/KIRREL3/ATF5/INHBA/EPHA5/HTR5A/LMX1A/NRG3/BCL11B/DLX2/PHACTR1/NEFL/ZIC1/OXTR/ATP1A2/H2AX/FAT4/KIF14/MDGA1/WDR62/RTN4RL1/NTRK2/NEUROD6/SLC7A11/ARHGAP11B/CXCR4/CNTN2/SMO/SALL1/TMEM108/EOMES/HES5
## GO:0030879 ROBO1/ATP2C2/RPLP0/NEURL1/SLC29A2/APRT/PHB2/GHRHR/HOXA5/BSX/SRC/SLC29A1/SLC6A3/TNFRSF11A/NRG3/CAV3/ZBTB7B/LRP5L/HOXA9/EPHA2/OXTR/TGFA/FGF10/LRP5/CDO1/ITGA2/ARG1/PAM/CYP19A1/STAT5A/RXFP1/SMO/CAV1/HK2
## GO:0048568 PAX6/HLX/PBX3/PBX1/MTHFD1/RPL38/RNF112/USH1G/PDGFA/COL13A1/NOTO/PDGFC/TEAD3/EN2/NDRG4/E2F8/TEAD1/TBC1D23/GDNF/HOXA5/GLI1/KRT19/LHX1/LBX1/NOTCH2/FZD5/ATP8A2/TAL1/ATF4/HOXA3/PITX2/WNT9B/TP53/DLX5/ZFPM2/IFT122/HPN/CCDC40/FBN2/PKDCC/EYA1/HMX3/NKX3-2/HOXA4/EDN1/EN1/DLX2/RPL10/OSR2/FGF9/SNAI1/HOXA7/HOXA9/GRHL3/EPHA2/ZIC1/TMIE/TBX4/APLNR/HOXB3/NKX2-5/ITGA8/NEUROG1/CITED2/FGF10/STRC/EFEMP1/RSPO3/ZFPM1/TGFB1/TEAD4/CDH23/TBX15/POU3F4/ALX3/VASH2/PCSK5/WNT16/DLL1/SMO/SALL1/SLC44A4/SOX15/EOMES/HOXA1/RBPMS2/TTPA/FZD6
## GO:0023061 SLC6A4/ADCY5/RPH3A/PCK2/TM7SF3/ABCC4/NRXN2/SYT11/GHRHR/GDNF/PRKCG/CAMK2A/CSPG5/ISL1/FOXA2/NNAT/SNCAIP/MAFA/SYT5/CADPS2/CD38/SNCA/NOS2/CCKAR/TFAP2B/SYT12/ABCC8/SYT4/GPR68/KCNA5/CHRNA5/NKX6-1/SLC4A8/SYK/KCNA2/GRM4/CFTR/INHBA/STXBP1/EPHA5/ACSL4/TNFRSF11A/ABAT/AGTR1/DOC2B/GABBR1/RPH3AL/TSPO/LIN7A/FKBP1B/CPE/AGT/SOX4/EDN1/GRIN3A/SV2A/FZD4/PFKM/WNT7A/ADORA1/PTPRN2/LRP5L/OTOF/PRKCB/MAOB/RAB11FIP1/SLC9B2/NR1H4/OXTR/ABCA1/CADPS/LRP5/MC4R/ASIC1/ICA1/ADRA2A/GLUL/SYT2/CYP19A1/PCSK5/APBA1/TFR2/SLC44A4/HCAR2/P2RY1/TACR1/EDN3/SYT1/F2RL1/VSNL1
## GO:1990573 KCNJ5/FXYD2/KCNJ9/WNK3/ABCC8/KCNJ10/SLC12A2/SLC12A8/KCNJ8/KCNJ16/HCN4/KCNJ1/ATP1A2/KCNJ3/KCNJ12/KCNJ15/SLC12A5
## GO:0070252 PDE4B/KCNJ5/SCN4B/KCNA5/STC1/GPD1L/CCDC88C/TNNT2/SCN2B/SCN1B/CAV3/KCNJ8/HCN4/ADORA1/KCNE5/EMP2/PDE4D/ATP1A2/KCNE3/KCNJ3/KCND3/MYH7/CACNA1G/ACTA2/DSC2/MYL1/CAV1
## GO:0002027 KCNJ5/SCN4B/ISL1/KCNA5/GPD1L/SCN2B/SCN1B/FKBP1B/CAV3/AGT/EDN1/HCN4/KCNE5/PDE4D/ADM2/KCNE3/KCNJ3/EDN2/BVES/KCND3/MYH7/TNNI3K/SLC1A1/CACNA1G/ADRA1B/DSC2/CAV1/EDN3
## GO:0048708 PAX6/BMP2/MAPT/F2/ROR2/GFAP/LRP1/SOX8/POU3F2/NTRK3/GPR37L1/TTBK1/PLP1/TSPAN2/MT3/ROR1/C5AR1/DLL1/VAX1/CNTN2/SMO/EOMES/HES5
## GO:0010975 ROBO1/KIAA0319/RGMA/PLXNC1/RIT2/ULK2/TLX2/MAPT/RND2/SEMA4D/ALKAL1/SDC2/DGKG/MAP6/CDC20/NDRG4/DISC1/ROR2/HECW2/EPHA7/TRIM67/GFAP/PQBP1/NOVA2/FEZF2/DCC/SLITRK1/PAK3/PLK5/POU3F2/CD38/ATP8A2/CRTC1/SPOCK1/CREB3L2/BCL11A/HECW1/EPHB3/CDK5R1/TNR/NTRK3/INPP5F/MAP2/ARSB/NEDD4/CDH4/PLXNB3/AMIGO1/LZTS3/PTPRO/ARC/NEU4/SLIT1/KEL/SCN1B/FKBP1B/SRCIN1/CSMD3/NTNG1/AGT/PTN/DPYSL3/MT3/WNT7A/PPFIA2/NEFL/NDNF/ROR1/ITPKA/FN1/DNM3/NRCAM/SERPINF1/CDH1/BAIAP2/ID1/TIAM1/RTN4RL1/NTRK2/TANC2/FES/KLK6/ULK4/CNTN2/SEMA3E/MINAR1/RET
## GO:0043931 BMP2/RFLNA/SEMA4D/THBS3/CCDC154/DCHS1/ADAMTS7/SNX10/XYLT1/PHOSPHO1/ZBTB16
## GO:0000462 RPS8/RPS19/RPS14/RRS1/RPP40/RPS21/RPS16/NOP14/UTP20/WDR43/BYSL/RCL1/DCAF13/PWP2
## GO:0007595 RPLP0/NEURL1/SLC29A2/APRT/GHRHR/SLC29A1/SLC6A3/ZBTB7B/OXTR/CDO1/PAM/STAT5A/CAV1/HK2
## GO:0140694 RPL5/NPM1/RPL6/RPL11/RPS3/RPLP0/RPS19/RPS27/RPS14/NOP53/RRS1/RPL38/EIF2A/RCC1/RPS5/ABRAXAS1/MAPT/NIP7/RPL24/MAPK9/RPS23/RPF2/MRTO4/CDC20/RPS28/TTN/RPSA/SQLE/KRT19/NEK7/RPS15/RPL23A/BOP1/SRC/PPAN/NEB/CASQ1/MDN1/TPX2/PRKDC/AURKA/C1QBP/HSPA1B/TNNT2/CCDC78/CAV3/EDN1/CDCA8/RPL10/AURKB/ACTG1/CHEK2/CEP152/SH3PXD2B/TNNT3/MAPK15/TMOD1/C10orf90/CSRP2/NKX2-5/CENPE/BIRC5/KIF11/KIFC1/KIF23/MYH7/WDR62/DLGAP5/NEBL/ACTA1/MYBL2/CHMP4C/PWP2/PRKAA2/MYL9/MYH11/DEUP1
## GO:1901796 MYC/RPS7/RPL5/NPM1/RPL26/RPL23/RPL11/NOP53/RRS1/RPS20/RPL37/RPF2/RPS15/BOP1/DYRK2/TP53/DYRK3/AURKA/MUC1/SOX4/AURKB/CHD5/CHEK2/SNAI1/CD44/BDKRB2/SNAI2/PMAIP1
## GO:0007193 NPR3/ADCY5/P2RY12/OPRD1/GRM4/HTR5A/GRM8/GPR37L1/GABBR1/EDN1/OXER1/GRIK3/ADORA1/AKAP5/PTGDR2/GRM7/CHRM4/ADRA2A/CHRM3/GABBR2/GRM1/P2RY1
## GO:0015800 SLC1A6/SNCA/SYT4/KCNJ10/STXBP1/ABAT/GABBR1/SLC12A2/LRRC8E/NTSR1/ADORA1/GRM7/ATP1A2/SLC1A1/NTRK2/SLC3A1/SLC7A11/APBA1/GRM1/SLC6A13
## GO:0007157 IGSF21/PVR/SELE/AMIGO2/CDH4/AMIGO1/ALCAM/TENM1/NECTIN4/DCHS1/CEACAM19/TENM4/CXADR/FAT4/SELP
## GO:0051444 RPS7/RPL5/RPL23/RPL11/RPS20/RPL37/CDC20/RPS15/BUB1B/MAD2L1
## GO:0021534 SLC6A4/LHX5/PSMG1/GLI1/LHX1/ATF5/GPR37L1/SMO
## GO:0021924 SLC6A4/LHX5/PSMG1/GLI1/LHX1/ATF5/GPR37L1/SMO
## GO:0021930 SLC6A4/LHX5/PSMG1/GLI1/LHX1/ATF5/GPR37L1/SMO
## GO:0070977 BMP2/RFLNA/SEMA4D/THBS3/CCDC154/DCHS1/ADAMTS7/SNX10/XYLT1/PHOSPHO1/ZBTB16
## GO:0048799 BMP2/RFLNA/SEMA4D/THBS3/CCDC154/DCHS1/ADAMTS7/SNX10/XYLT1/PHOSPHO1/ZBTB16/RET
## GO:0042363 CYP27B1/CYP26A1/CYP2W1/CRABP1/CYP4F12/CYP4F11/CYP4F3
## GO:1904816 CCT3/CCT5/CCT4/CCT2/GNL3/CCT8/CCT6A
## GO:0042476 BMP2/AQP5/HDAC1/AQP6/BSG/ROGDI/EDA/RSPO2/JAG2/SRC/SLC4A2/FAM20C/PITX2/CFTR/INHBA/APCDD1/CCDC154/EDN1/BCL11B/DLX2/OSR2/TNFRSF11B/ENAM/SNX10/FGF10/WNT10A/PAM/FST/SMO/TSPEAR/EDAR/DSPP
## GO:0014068 PDGFA/PRR5/SEMA4D/PDGFC/FGFR1/F2/ROR2/SRC/NTRK3/NEDD4/AGT/ROR1/FN1/FLT3/TEK/CD28/NTRK2/MYOC/WNT16/SEMA3E/SELP/F2RL1
## GO:1904064 LRRC38/KCNA1/RGN/CRACR2A/F2/FXYD2/NLGN3/WNK3/SNCA/GSTO1/CACNG4/TESC/ABCC8/TRPC3/CASQ1/ANTKMT/P2RX3/AMIGO1/ARC/LRRC26/PLP1/NOS1/AGT/EDN1/NTSR1/CEMIP/AKAP5/PPP3R2/KCNE5/PIRT/APLNR/ADCYAP1R1/KCNC1/BDKRB1/CNKSR3/CACNB3/LRRC55/CAV1/EDN3
## GO:0032409 LRRC38/GSG1L/FXYD6/PDE4B/CABP2/FGF14/KCNA1/KCNG1/RGN/RASGRF1/CRACR2A/PHB2/HECW2/FXYD2/NLGN3/SCN4B/CACNG7/WNK3/SNCA/GSTO1/CACNG4/TESC/ABCC8/JPH1/HECW1/JSRP1/CASQ1/ANTKMT/CFTR/NEDD4/GPD1L/AMIGO1/ARC/SCN2B/LRRC26/SCN1B/FKBP1B/CAV3/NOS1/EDN1/NTSR1/SHISA7/HCN4/AKAP5/PPP3R2/KCNE5/PIRT/PDE4D/ATP1A2/KCNC1/KCNE3/SHANK1/STOM/ADRA2A/CNKSR3/CHRM3/CNIH2/CACNB3/LRRC55/CAV1/MMP9
## GO:0086003 PDE4B/KCNJ5/SCN4B/KCNA5/STC1/GPD1L/SCN2B/SCN1B/CAV3/KCNJ8/HCN4/ADORA1/KCNE5/PDE4D/ATP1A2/KCNE3/KCNJ3/KCND3/CACNA1G/DSC2/CAV1
## GO:0003012 MYOG/PDE4B/MYOD1/KCNJ5/KCNA1/FBXO32/SCNN1B/GAMT/TTN/SCN4B/GDNF/ENO1/CD38/ATP8A2/TRIM72/GSTO1/ABCC8/KCNA5/PROK2/DES/JSRP1/TRPC3/CASQ1/INPP5F/CHRNA1/STC1/P2RX3/GPD1L/ABAT/SULF2/CHRND/GRIP2/TNNT2/SCN2B/CLCN1/CCDC78/SCN1B/FKBP1B/CAV3/NOS1/SCO2/AGT/EDN1/KCNJ8/HCN4/ADORA1/KCNE5/TNNT3/OXTR/PDE4D/TMOD1/SPHK1/MYOT/NKX2-5/ATP1A2/NEUROG1/KCNE3/KCNJ3/EDN2/ITGA2/BDKRB2/KCND3/MYH7/ADRA2A/ACTA1/TNNI3K/KCNJ12/CHRM3/MYOC/CACNA1G/MYL9/ADRA1B/SLC6A8/CXCR4/ACTA2/DSC2/MYH11/MYL1/PGAM2/P2RY1/TACR1/CAV1/EDN3
## GO:0086011 KCNJ5/KCNA5/KCNH7/SCN2B/KCNJ8/KCNE5/KCNE3/KCNJ3/KCND3/CACNB3/CAV1
## GO:0001504 SLC6A4/SLC29A2/SLC1A6/GFAP/GDNF/SLC6A12/SNCA/KCNJ10/SLC29A1/SLC6A3/SLC18A2/NOS1/ATP1A2/GPM6B/SLC6A13
## GO:0030490 RPS8/RPS19/RPS14/RRS1/RPP40/NOB1/RPS21/RPS16/RPS28/WDR3/NOP14/UTP20/WDR43/BYSL/RCL1/DCAF13/PWP2
## GO:0043266 LRRC38/KCNIP1/KCNA1/KCNG1/FXYD2/DPP6/WNK3/ATF4/ABCC8/KCNA5/NEDD4/AMIGO1/LRRC26/KEL/CAV3/NOS1/ADORA1/KCNIP3/KCNE5/KCNC1/KCNE3/ADRA2A/DPP10/LRRC55/KCNIP4/CAV1/EDN3
## GO:0015844 SLC6A4/SYT11/GDNF/SYT5/SNCA/SYT12/SYT4/SYK/SLC6A3/KCNA2/SLC18A2/ABAT/GABBR1/NOS1/AGT/MAOB/OXTR/MAPK15/GPM6B/ADRA2A/SYT2/P2RY1/SYT1
## Count
## GO:0002181 98
## GO:0042255 28
## GO:0007156 53
## GO:0042273 31
## GO:0098742 72
## GO:0042274 29
## GO:0000027 15
## GO:0042254 75
## GO:0034765 109
## GO:0050804 96
## GO:0099177 96
## GO:0098657 61
## GO:0061564 104
## GO:0050808 93
## GO:0006364 56
## GO:0071805 53
## GO:0097485 58
## GO:0001764 47
## GO:0006813 57
## GO:0050878 81
## GO:0016072 62
## GO:0007411 57
## GO:0007409 92
## GO:0006836 52
## GO:0043270 65
## GO:0030900 82
## GO:2000027 36
## GO:0000028 10
## GO:0042391 88
## GO:1904062 80
## GO:0048880 84
## GO:0098659 33
## GO:0099587 33
## GO:0021953 44
## GO:0007589 25
## GO:0001505 52
## GO:0150063 82
## GO:1904667 8
## GO:0098739 47
## GO:0006936 73
## GO:0001654 81
## GO:0032412 60
## GO:0006865 37
## GO:0022613 91
## GO:0022898 61
## GO:0030198 66
## GO:0098656 58
## GO:0043062 66
## GO:0015701 12
## GO:0045229 66
## GO:0010959 81
## GO:0021537 56
## GO:0030879 34
## GO:0048568 88
## GO:0023061 90
## GO:1990573 17
## GO:0070252 27
## GO:0002027 28
## GO:0048708 23
## GO:0010975 87
## GO:0043931 11
## GO:0000462 14
## GO:0007595 14
## GO:0140694 77
## GO:1901796 28
## GO:0007193 22
## GO:0015800 20
## GO:0007157 15
## GO:0051444 10
## GO:0021534 8
## GO:0021924 8
## GO:0021930 8
## GO:0070977 11
## GO:0048799 12
## GO:0042363 7
## GO:1904816 7
## GO:0042476 32
## GO:0014068 22
## GO:1904064 39
## GO:0032409 61
## GO:0086003 21
## GO:0003012 83
## GO:0086011 11
## GO:0001504 15
## GO:0030490 17
## GO:0043266 27
## GO:0015844 23dotplot(res_GO)
10 Pathway-based analysis with rROMA
Large-scale molecular data, such as transcriptomics and proteomics, offers great opportunities for understanding the complexity of biological processes. One important aspect of data analysis in systems biology is the shift from a reductionist approach that focuses on individual components to a more integrative perspective that considers the system as a whole, where the emphasis shifted from differential expression of individual genes to determining the activity of gene sets.
The rROMA algorithm is used for computation of the activity of gene sets with coordinated expression.
rRoma requires a gene expression matrix, with column names indicating samples and row names indicating gene names.
It also requires a module file containing information on the genesets that need to be evaluated. The module file can be loaded from a GMT file.
The figure below illustrates a schematic diagram of the workflow of the rROMA algorithm.
10.1 Input dataset
Warning: rRoma works with normalized data. Here the normalized TB dataset is used.
summary(log.rna.norm)## TB236 TB240 TB242 TB243
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 3.014 1st Qu.: 3.361 1st Qu.: 2.743 1st Qu.: 3.064
## Median : 5.599 Median : 5.695 Median : 5.547 Median : 5.599
## Mean : 4.910 Mean : 4.996 Mean : 4.801 Mean : 4.895
## 3rd Qu.: 6.865 3rd Qu.: 6.825 3rd Qu.: 6.875 3rd Qu.: 6.840
## Max. :11.559 Max. :12.274 Max. :12.502 Max. :11.694
## TB244 TB248 TB249 TB252
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 2.937 1st Qu.: 2.943 1st Qu.: 3.106 1st Qu.: 2.777
## Median : 5.541 Median : 5.652 Median : 5.640 Median : 5.614
## Mean : 4.872 Mean : 4.927 Mean : 4.903 Mean : 4.842
## 3rd Qu.: 6.864 3rd Qu.: 6.932 3rd Qu.: 6.868 3rd Qu.: 6.889
## Max. :11.480 Max. :12.054 Max. :11.423 Max. :12.332
## TB254 TB256 TB262 TB263
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 2.951 1st Qu.: 3.352 1st Qu.: 3.037 1st Qu.: 3.038
## Median : 5.609 Median : 5.701 Median : 5.659 Median : 5.562
## Mean : 4.882 Mean : 4.974 Mean : 4.890 Mean : 4.883
## 3rd Qu.: 6.879 3rd Qu.: 6.863 3rd Qu.: 6.845 3rd Qu.: 6.828
## Max. :11.854 Max. :11.468 Max. :11.405 Max. :11.127
## TB264 TB267 TB268 TB269
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 3.297 1st Qu.: 3.269 1st Qu.: 3.460 1st Qu.: 2.836
## Median : 5.694 Median : 5.627 Median : 5.605 Median : 5.591
## Mean : 4.971 Mean : 4.956 Mean : 4.994 Mean : 4.858
## 3rd Qu.: 6.831 3rd Qu.: 6.820 3rd Qu.: 6.810 3rd Qu.: 6.896
## Max. :11.434 Max. :11.141 Max. :11.292 Max. :11.854
## TB271 TB274 TB277 TB279
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 3.060 1st Qu.: 3.041 1st Qu.: 3.039 1st Qu.: 3.070
## Median : 5.638 Median : 5.640 Median : 5.556 Median : 5.700
## Mean : 4.903 Mean : 4.901 Mean : 4.886 Mean : 4.929
## 3rd Qu.: 6.832 3rd Qu.: 6.844 3rd Qu.: 6.838 3rd Qu.: 6.869
## Max. :11.615 Max. :12.933 Max. :11.408 Max. :12.296
## TB280 TB284 TB288 TB290
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 2.945 1st Qu.: 3.061 1st Qu.: 2.828 1st Qu.: 2.574
## Median : 5.544 Median : 5.682 Median : 5.544 Median : 5.522
## Mean : 4.833 Mean : 4.931 Mean : 4.817 Mean : 4.768
## 3rd Qu.: 6.791 3rd Qu.: 6.863 3rd Qu.: 6.867 3rd Qu.: 6.866
## Max. :11.671 Max. :12.284 Max. :10.915 Max. :11.612
## TB292 TB295 TB296 TB299
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 3.124 1st Qu.: 3.304 1st Qu.: 2.689 1st Qu.: 3.134
## Median : 5.588 Median : 5.611 Median : 5.644 Median : 5.658
## Mean : 4.920 Mean : 4.974 Mean : 4.816 Mean : 4.931
## 3rd Qu.: 6.854 3rd Qu.: 6.845 3rd Qu.: 6.855 3rd Qu.: 6.854
## Max. :11.461 Max. :11.090 Max. :11.815 Max. :12.186
## TB300 TB303 TB305 TB081
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 3.177 1st Qu.: 3.277 1st Qu.: 3.290 1st Qu.: 3.176
## Median : 5.617 Median : 5.619 Median : 5.724 Median : 5.627
## Mean : 4.928 Mean : 4.967 Mean : 4.997 Mean : 4.931
## 3rd Qu.: 6.817 3rd Qu.: 6.844 3rd Qu.: 6.855 3rd Qu.: 6.851
## Max. :11.030 Max. :11.493 Max. :11.300 Max. :11.067
## TB084 TB085 TB087 TB102
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 2.939 1st Qu.: 2.813 1st Qu.: 3.222 1st Qu.: 2.741
## Median : 5.720 Median : 5.555 Median : 5.700 Median : 5.595
## Mean : 4.902 Mean : 4.854 Mean : 4.968 Mean : 4.819
## 3rd Qu.: 6.872 3rd Qu.: 6.899 3rd Qu.: 6.825 3rd Qu.: 6.865
## Max. :12.914 Max. :12.369 Max. :12.296 Max. :11.136
## TB103 TB114 TB118 TB121
## Min. : 0.000 Min. : 0.000 Min. : 0.000 Min. : 0.000
## 1st Qu.: 3.148 1st Qu.: 2.927 1st Qu.: 2.525 1st Qu.: 3.137
## Median : 5.652 Median : 5.655 Median : 5.609 Median : 5.622
## Mean : 4.914 Mean : 4.893 Mean : 4.801 Mean : 4.902
## 3rd Qu.: 6.817 3rd Qu.: 6.855 3rd Qu.: 6.952 3rd Qu.: 6.827
## Max. :11.669 Max. :12.868 Max. :13.090 Max. :11.489
## TB137
## Min. : 0.000
## 1st Qu.: 3.400
## Median : 5.734
## Mean : 5.009
## 3rd Qu.: 6.810
## Max. :13.147We can then load sample labels:
Group <- design$Group
names(Group) <- rownames(design)
table(Group)## Group
## G1 G2 G3 G4
## 4 11 13 1310.2 Selecting the module list
Here, we extract all the “HALLMARK” gene sets from MSigDB (Liberzon A et al, Cell Syst 2015). The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. The collection of “Hallmark” gene sets as part of MSigDB consists of “refined” gene sets describing biological states or processes.
Note that we chose the Hallmark database for explanatory purpose. To have a more complete view of biological systems at stake in your study, we recommend using rRoma with multiple databases.
Hallmarks <- SelectFromMSIGdb("HALLMARK")## [1] "Searching in MsigDB v6.2"
## [1] "The following genesets have been selected:"
## [1] "HALLMARK_TNFA_SIGNALING_VIA_NFKB (200 genes)"
## [2] "HALLMARK_HYPOXIA (200 genes)"
## [3] "HALLMARK_CHOLESTEROL_HOMEOSTASIS (74 genes)"
## [4] "HALLMARK_MITOTIC_SPINDLE (200 genes)"
## [5] "HALLMARK_WNT_BETA_CATENIN_SIGNALING (42 genes)"
## [6] "HALLMARK_TGF_BETA_SIGNALING (54 genes)"
## [7] "HALLMARK_IL6_JAK_STAT3_SIGNALING (87 genes)"
## [8] "HALLMARK_DNA_REPAIR (150 genes)"
## [9] "HALLMARK_G2M_CHECKPOINT (200 genes)"
## [10] "HALLMARK_APOPTOSIS (161 genes)"
## [11] "HALLMARK_NOTCH_SIGNALING (32 genes)"
## [12] "HALLMARK_ADIPOGENESIS (200 genes)"
## [13] "HALLMARK_ESTROGEN_RESPONSE_EARLY (200 genes)"
## [14] "HALLMARK_ESTROGEN_RESPONSE_LATE (200 genes)"
## [15] "HALLMARK_ANDROGEN_RESPONSE (101 genes)"
## [16] "HALLMARK_MYOGENESIS (200 genes)"
## [17] "HALLMARK_PROTEIN_SECRETION (96 genes)"
## [18] "HALLMARK_INTERFERON_ALPHA_RESPONSE (97 genes)"
## [19] "HALLMARK_INTERFERON_GAMMA_RESPONSE (200 genes)"
## [20] "HALLMARK_APICAL_JUNCTION (200 genes)"
## [21] "HALLMARK_APICAL_SURFACE (44 genes)"
## [22] "HALLMARK_HEDGEHOG_SIGNALING (36 genes)"
## [23] "HALLMARK_COMPLEMENT (200 genes)"
## [24] "HALLMARK_UNFOLDED_PROTEIN_RESPONSE (113 genes)"
## [25] "HALLMARK_PI3K_AKT_MTOR_SIGNALING (105 genes)"
## [26] "HALLMARK_MTORC1_SIGNALING (200 genes)"
## [27] "HALLMARK_E2F_TARGETS (200 genes)"
## [28] "HALLMARK_MYC_TARGETS_V1 (200 genes)"
## [29] "HALLMARK_MYC_TARGETS_V2 (58 genes)"
## [30] "HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION (200 genes)"
## [31] "HALLMARK_INFLAMMATORY_RESPONSE (200 genes)"
## [32] "HALLMARK_XENOBIOTIC_METABOLISM (200 genes)"
## [33] "HALLMARK_FATTY_ACID_METABOLISM (158 genes)"
## [34] "HALLMARK_OXIDATIVE_PHOSPHORYLATION (200 genes)"
## [35] "HALLMARK_GLYCOLYSIS (200 genes)"
## [36] "HALLMARK_REACTIVE_OXIGEN_SPECIES_PATHWAY (49 genes)"
## [37] "HALLMARK_P53_PATHWAY (200 genes)"
## [38] "HALLMARK_UV_RESPONSE_UP (158 genes)"
## [39] "HALLMARK_UV_RESPONSE_DN (144 genes)"
## [40] "HALLMARK_ANGIOGENESIS (36 genes)"
## [41] "HALLMARK_HEME_METABOLISM (200 genes)"
## [42] "HALLMARK_COAGULATION (138 genes)"
## [43] "HALLMARK_IL2_STAT5_SIGNALING (200 genes)"
## [44] "HALLMARK_BILE_ACID_METABOLISM (112 genes)"
## [45] "HALLMARK_PEROXISOME (104 genes)"
## [46] "HALLMARK_ALLOGRAFT_REJECTION (200 genes)"
## [47] "HALLMARK_SPERMATOGENESIS (135 genes)"
## [48] "HALLMARK_KRAS_SIGNALING_UP (200 genes)"
## [49] "HALLMARK_KRAS_SIGNALING_DN (200 genes)"
## [50] "HALLMARK_PANCREAS_BETA_CELLS (40 genes)"Hallmarks <- lapply(Hallmarks, function(x){
x$Name <- sub("HALLMARK_", "", x$Name)
x
})In case we already have a GMT file with modules we want to test, we can also load them by using the ReadGMTFile function.
#myDB <- ReadGMTFile("h.all.v6.1.symbols.gmt", SearchString = NULL, Mode = "ANY")10.3 Performing rROMA: basics
We can now run rRoma on our dataset by simply specifying the expression dataset and the modules you want to test. Here we are fixing the seed for used for random permutations.
# set.seed(69)
rRoma.output <- rRoma.R(log.rna.norm, Hallmarks)10.4 Reading the output
The most important information can be found in the module matrix, here in rRoma.output$ModuleMatrix. It contains p and q values for overdispersion (L1) and shift (Median Exp) for all tested modules.
head(rRoma.output$ModuleMatrix)## L1 Median L1 ppv L1 L1/L2 Median L1/L2
## TNFA_SIGNALING_VIA_NFKB 0.1321127 0.1553025 0.96 0.9991029 1.248812
## HYPOXIA 0.1538242 0.1553025 0.54 1.0983751 1.248812
## CHOLESTEROL_HOMEOSTASIS 0.2230479 0.1870367 0.14 1.6991443 1.325560
## MITOTIC_SPINDLE 0.2243646 0.1553025 0.00 1.6896764 1.248812
## WNT_BETA_CATENIN_SIGNALING 0.3488909 0.2260947 0.02 2.7078359 1.459496
## TGF_BETA_SIGNALING 0.2802496 0.1982851 0.05 1.7808915 1.334400
## ppv L1/L2 Median Exp ppv Median Exp q L1
## TNFA_SIGNALING_VIA_NFKB 1.00 -1.22608089 0.00 0.98979592
## HYPOXIA 0.88 -0.73655754 0.00 0.72500000
## CHOLESTEROL_HOMEOSTASIS 0.11 0.58965308 0.00 0.33333333
## MITOTIC_SPINDLE 0.02 0.11061512 0.46 0.00000000
## WNT_BETA_CATENIN_SIGNALING 0.00 0.09990226 0.70 0.07142857
## TGF_BETA_SIGNALING 0.11 -0.33013768 0.11 0.13888889
## q L1/L2 q Median Exp
## TNFA_SIGNALING_VIA_NFKB 1.0000000 0.00000000
## HYPOXIA 1.0000000 0.00000000
## CHOLESTEROL_HOMEOSTASIS 0.3666667 0.00000000
## MITOTIC_SPINDLE 0.1111111 0.68888235
## WNT_BETA_CATENIN_SIGNALING 0.0000000 0.70480392
## TGF_BETA_SIGNALING 0.3666667 0.04941176We are interested in two different types of modules:
- Shifted modules, whose genes behave differently from the rest of the genes in at least one sample. Corresponding p value is in ppv Median Exp. A q value is also calculated and given in q Median Exp. Here we will consider a module as shifted if p < 0.05. We don’t look at q values here as both the number of samples and the number of tested modules is small. Consider looking at q values for larger data sets.
- Overdispersed modules, for which the approximation to one PC is correct. Corresponding p value is in ppv ML1. A q value is also calculated and given in q L1. Here we will consider a module as overdispersed if p < 0.05. We don’t look at q values for the same reason as before, but considered looking at it for larger datasets10.4.1 Shifted modules
We are first interested in shifted modules.
shifted.modules <- which(rRoma.output$ModuleMatrix[, "ppv Median Exp"] <= 0.05)We want to see which samples are responsible for the shift. This can be done by looking at Sample Scores. The function plots a heatmap of these score:
Plot.Genesets.Samples(rRoma.output, Selected = shifted.modules, GroupInfo = Group, cluster_cols = TRUE)## [1] "30 genesets selected"
## [1] "41 samples have an associated group"
This representation can help us define groups of samples that behave similarly on a pathway level.
10.4.2 Overdispersed modules
overdispersed.modules <- which(rRoma.output$ModuleMatrix[, "ppv L1"] <= 0.05 & rRoma.output$ModuleMatrix[, "ppv Median Exp"] > 0.05)We first plot the same heatmap as before to visualize sample scores:
Plot.Genesets.Samples(rRoma.output, Selected = overdispersed.modules, GroupInfo = Group, cluster_cols = TRUE)## [1] "8 genesets selected"
## [1] "41 samples have an associated group"